{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一.算法介绍\n",
    "层次聚类试图在不同层次对数据集进行划分，从而形成树形的聚类结构。数据集的划分有两种策略：一种是自下而上，另一种是自上而下；自下而上初始将每个样本视作一个单独的簇，然后选择相距最近的两个样本进行合并，循环执行，直到达到预设的聚类簇个数，而自上而下恰好相反，它假设所有样本都属于同一个簇，然后将相距最远样本进行划分....，这一节我们实现AGglomerative NESting（AGNES）算法，一种自下而上的算法，它的原理其实刚刚已经提到了：    \n",
    "\n",
    "**相距最近的两个簇进行合并，直至达到预设的聚类簇个数**  \n",
    "\n",
    "所以，核心问题变成如何度量两个簇之间的距离，这在上一节已经做过介绍了，AGNES通常采用类间最小、最大、平均距离这三种，分别对应的算法被称作“单链接”，“全链接”和“均链接”，其聚类过程可以类似如下的“树状图”，[图片来源于>>>](https://blog.csdn.net/weixin_44530236/article/details/89160137)：   \n",
    "\n",
    "![avatar](./source/18_聚类_AGNES.png)  \n",
    "\n",
    "这里，横坐标表示样本id，纵坐标表示类间距离，树形图即是我们的聚类过程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 二.算法流程\n",
    ">输入：样本集$D=\\{x_1,x_2,...,x_m\\}$;聚类簇距离度量函数$d$;聚类簇数$k$；  \n",
    "\n",
    ">过程：\n",
    "\n",
    ">（1） 对$j=1,2,...,m$\n",
    ">> $G_j=\\{x_j\\}$  \n",
    "\n",
    ">（2） 对$i=1,2,..,m$\n",
    ">> 对$j=1,2,...,m$\n",
    ">>> \n",
    "$$\n",
    "M(i,j)=d(G_i,G_j)\\\\\n",
    "M(j,i)=M(i,j)\n",
    "$$\n",
    "\n",
    ">（3） 设置当前聚类簇个数$q=m$，当$q>k$时，循环执行以下过程  \n",
    ">>（3.1）找出距离最近的两个聚类簇$G_{i^*}$和$G_{j^*}$（$i^*<j^*$）；   \n",
    "\n",
    ">>（3.2）合并$G_{i^*}$和$G_{j^*}$：$G_{i^*}=G_{i^*}\\bigcup G_{j^*}$；   \n",
    "\n",
    ">>（3.3） 对$j=j^*+1,j^*+2,...,q$  \n",
    "\n",
    ">>> 将聚类簇$G_j$重新编号为$G_{j-1}$\n",
    "\n",
    ">>（3.4）删除距离矩阵$M$的第$j^*$行与第$j^*$列（注意，矩阵的行列都要减1）  \n",
    "\n",
    ">>（3.5）对$j=1,2,...,q-1$\n",
    "$$\n",
    "M(i^*,j)=d(G_{i^*},G_j)\\\\\n",
    "M(j,i^*)=M(i^*,j)\n",
    "$$\n",
    ">>（3.6）$q=q-1$  \n",
    "\n",
    ">输出$G=\\{G_1,G_2,...,G_k\\}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三.代码实现\n",
    "为了代码简便，距离计算就只实现了：（1）样本距离为欧氏距离；（2）类间距离为平均距离的情况，其他组合情况，还请自行定义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "层次聚类：AGNES的实现，代码封装在ml_models.agnes\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# 定义默认的距离函数\n",
    "def euclidean_average_dist(Gi, Gj):\n",
    "    return np.sum(np.power(np.mean(Gi, axis=0) - np.mean(Gj, axis=0), 2))\n",
    "\n",
    "\n",
    "class AGNES(object):\n",
    "    def __init__(self, k=3, dist_method=None):\n",
    "        \"\"\"\n",
    "        :param k: 聚类数量\n",
    "        :param dist_method: 距离函数定义\n",
    "        \"\"\"\n",
    "        self.k = k\n",
    "        self.dist_method = dist_method\n",
    "        if self.dist_method is None:\n",
    "            self.dist_method = euclidean_average_dist\n",
    "        self.G = None\n",
    "        self.cluster_center = {}  # 记录聚类中心点\n",
    "\n",
    "    def fit(self, X):\n",
    "        m, _ = X.shape\n",
    "        # 初始化簇\n",
    "        G = {}\n",
    "        for row in range(m):\n",
    "            G[row] = X[[row]]\n",
    "        # 计算簇间距离\n",
    "        M = np.zeros(shape=(m, m))\n",
    "        for i in range(0, m):\n",
    "            for j in range(0, m):\n",
    "                M[i, j] = self.dist_method(G[i], G[j])\n",
    "                M[j, i] = M[i, j]\n",
    "        q = m\n",
    "        while q > self.k:\n",
    "            # 寻找最近的簇\n",
    "            min_dist = np.infty\n",
    "            i_ = None\n",
    "            j_ = None\n",
    "            for i in range(0, q - 1):\n",
    "                for j in range(i + 1, q):\n",
    "                    if M[i, j] < min_dist:\n",
    "                        i_ = i\n",
    "                        j_ = j\n",
    "                        min_dist = M[i, j]\n",
    "            # 合并\n",
    "            G[i_] = np.concatenate([G[i_], G[j_]])\n",
    "            # 重编号\n",
    "            for j in range(j_ + 1, q):\n",
    "                G[j - 1] = G[j]\n",
    "            # 删除G[q]\n",
    "            del G[q-1]\n",
    "            # 删除\n",
    "            M = np.delete(M, j_, axis=0)\n",
    "            M = np.delete(M, j_, axis=1)\n",
    "            # 更新距离\n",
    "            for j in range(q - 1):\n",
    "                M[i_, j] = self.dist_method(G[i_], G[j])\n",
    "                M[j, i_] = M[i_, j]\n",
    "            # 更新q\n",
    "            q = q - 1\n",
    "#         self.G = G\n",
    "        for idx in G:\n",
    "            self.cluster_center[idx] = np.mean(G[idx], axis=0)\n",
    "\n",
    "    def predict(self, X):\n",
    "        rst = []\n",
    "        rows, _ = X.shape\n",
    "        for row in range(rows):\n",
    "            # vec = X[[row]]\n",
    "            vec = X[row]\n",
    "            min_dist = np.infty\n",
    "            bst_label = None\n",
    "            for idx in self.cluster_center:\n",
    "                # dist = self.dist_method(vec, self.G[idx]) < min_dist\n",
    "                dist = np.sum(np.power(vec - self.cluster_center[idx], 2))\n",
    "                if dist < min_dist:\n",
    "                    bst_label = idx\n",
    "                    min_dist = dist\n",
    "            rst.append(bst_label)\n",
    "        return np.asarray(rst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 四.测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('../')\n",
    "from ml_models import utils\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "X, y = make_blobs(n_samples=400, centers=4, cluster_std=0.85, random_state=0)\n",
    "X = X[:, ::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#训练\n",
    "agnes = AGNES(k=4)\n",
    "agnes.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bbe432bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.plot_decision_function(X, y, agnes)\n",
    "utils.plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
